Temperature Scaling for Quantile Calibration
Deep learning models are often poorly calibrated, i.e., they may produce overconfident predictions that are wrong, implying that their uncertainty estimates are unreliable. While a number of approaches have been proposed recently to calibrate classification models, relatively little work exists on calibrating regression models. Temperature Scaling is one of the most popular methods for \emph{classification calibration}. It performs better than or equal to more sophisticated methods. We investigate the use of Temperature Scaling for \emph{regression calibration} under notion of quantile calibration.
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